{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:G3Q3B6OKGWZPBJNRQ7UNMSVMDI","short_pith_number":"pith:G3Q3B6OK","schema_version":"1.0","canonical_sha256":"36e1b0f9ca35b2f0a5b187e8d64aac1a3af93e08292e4b28f6f0e6a139ddd5f8","source":{"kind":"arxiv","id":"2607.27940","version":1},"attestation_state":"computed","paper":{"title":"TriShield: Zero-Utility-Loss Defense Against Privacy Backdoors in Federated Language Model Fine-Tuning via Orthogonal Gradient Projection and Optimizer State Entanglement","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CL"],"primary_cat":"cs.LG","authors_text":"Cheng Wei (Honor Device Co., China), Ltd., Shenzhen","submitted_at":"2026-07-30T09:49:13Z","abstract_excerpt":"Federated fine-tuning of large language models (LLMs) enables collaborative training without exposing raw data. However, a recent attack, NeuroImprint [1] (arXiv:2606.20553), demonstrates that a malicious parameter server can corrupt a PEFT adapter into a privacy backdoor: by assigning a dedicated memorization neuron to each training sample and ensuring each neuron updates at most once, the server can analytically reconstruct 59\\%--79\\% of client training data with high semantic fidelity. Existing defenses---including local differential privacy (LDP) [8] and gradient clipping---either fail aga"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2607.27940","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-07-30T09:49:13Z","cross_cats_sorted":["cs.CL"],"title_canon_sha256":"6aa7eeb1ba6a5b940f924ef5d0e4e84e017942951c91688367af38265ed09c12","abstract_canon_sha256":"ece63e575959f8f1254c2cbf373ef52b2c97a43834f03bace7ffefecf00106d3"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"36e1b0f9ca35b2f0a5b187e8d64aac1a3af93e08292e4b28f6f0e6a139ddd5f8","last_reissued_at":"2026-07-31T01:34:51.009510Z","signature_status":"unsigned_v0","first_computed_at":"2026-07-31T01:34:51.009510Z"},"graph_snapshot":{"paper":{"title":"TriShield: Zero-Utility-Loss Defense Against Privacy Backdoors in Federated Language Model Fine-Tuning via Orthogonal Gradient Projection and Optimizer State Entanglement","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CL"],"primary_cat":"cs.LG","authors_text":"Cheng Wei (Honor Device Co., China), Ltd., Shenzhen","submitted_at":"2026-07-30T09:49:13Z","abstract_excerpt":"Federated fine-tuning of large language models (LLMs) enables collaborative training without exposing raw data. However, a recent attack, NeuroImprint [1] (arXiv:2606.20553), demonstrates that a malicious parameter server can corrupt a PEFT adapter into a privacy backdoor: by assigning a dedicated memorization neuron to each training sample and ensuring each neuron updates at most once, the server can analytically reconstruct 59\\%--79\\% of client training data with high semantic fidelity. Existing defenses---including local differential privacy (LDP) [8] and gradient clipping---either fail aga"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.27940","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2607.27940/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2607.27940","created_at":"2026-07-31T01:34:51.012924+00:00"},{"alias_kind":"arxiv_version","alias_value":"2607.27940v1","created_at":"2026-07-31T01:34:51.012924+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.27940","created_at":"2026-07-31T01:34:51.012924+00:00"},{"alias_kind":"pith_short_12","alias_value":"G3Q3B6OKGWZP","created_at":"2026-07-31T01:34:51.012924+00:00"},{"alias_kind":"pith_short_16","alias_value":"G3Q3B6OKGWZPBJNR","created_at":"2026-07-31T01:34:51.012924+00:00"},{"alias_kind":"pith_short_8","alias_value":"G3Q3B6OK","created_at":"2026-07-31T01:34:51.012924+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/G3Q3B6OKGWZPBJNRQ7UNMSVMDI","json":"https://pith.science/pith/G3Q3B6OKGWZPBJNRQ7UNMSVMDI.json","graph_json":"https://pith.science/api/pith-number/G3Q3B6OKGWZPBJNRQ7UNMSVMDI/graph.json","events_json":"https://pith.science/api/pith-number/G3Q3B6OKGWZPBJNRQ7UNMSVMDI/events.json","paper":"https://pith.science/paper/G3Q3B6OK"},"agent_actions":{"view_html":"https://pith.science/pith/G3Q3B6OKGWZPBJNRQ7UNMSVMDI","download_json":"https://pith.science/pith/G3Q3B6OKGWZPBJNRQ7UNMSVMDI.json","view_paper":"https://pith.science/paper/G3Q3B6OK","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2607.27940&json=true","fetch_graph":"https://pith.science/api/pith-number/G3Q3B6OKGWZPBJNRQ7UNMSVMDI/graph.json","fetch_events":"https://pith.science/api/pith-number/G3Q3B6OKGWZPBJNRQ7UNMSVMDI/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/G3Q3B6OKGWZPBJNRQ7UNMSVMDI/action/timestamp_anchor","attest_storage":"https://pith.science/pith/G3Q3B6OKGWZPBJNRQ7UNMSVMDI/action/storage_attestation","attest_author":"https://pith.science/pith/G3Q3B6OKGWZPBJNRQ7UNMSVMDI/action/author_attestation","sign_citation":"https://pith.science/pith/G3Q3B6OKGWZPBJNRQ7UNMSVMDI/action/citation_signature","submit_replication":"https://pith.science/pith/G3Q3B6OKGWZPBJNRQ7UNMSVMDI/action/replication_record"}},"created_at":"2026-07-31T01:34:51.012924+00:00","updated_at":"2026-07-31T01:34:51.012924+00:00"}